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TERATEC 2018 Forum
Plenary sessions
Tuesday, June 19, 2018

Machine, Algorithms and Humans: shall we expect from the third AI wave to reshuffle the cards in the world of engineering and simulation?
Nicolas VAYATIS, directeur du CMLA et du master MVA de l'ENS de Paris-Saclay

Fig 1 - Prototype logiciel pour la génération automatique d'architectures viables de boîtes de vitesses (modèles filaires) à partir d'un cahier des charges paramétrable.	Fig 2 - Modèle volumique d'une boîte manuelle à 5 vitesses dont l'architecture est générée par un algorithme.

The third wave of artificial intelligence is much stronger than the previous ones sinces the automated modules can now rely on three key pillars: databases that are finally organized, increasing computing power, and the maturity of theories and algorithms in machine learning.

In the field of the internet and its major functionalities performed by digital engines (search, translation, targeting, recommendation), the spectacular capacities of the algorithms fit with the everyday needs of internet users, whatever their age, social status, cultural or geographical rooting.

In the physical world, after railways and airways, it has now become clear that roadways can also be ridden by driverless vehicles.

However, there are some specific domains such as engineering and simulation, but also healthcare and training, where AI-based technologies are only at the stage of proofs of concepts and encounter some obstacles in order to scale up. I find particularly interesting to examine why this is so and to understand which are the scientific and technological challenges to be addressed to make the industrialization possible. The role of field experts in this process will also be discussed.

Biography : Nicolas Vayatis is Full Professor at the Department of Mathematics of ENS Paris-Saclay and is the Director of the Center for Mathematics and Their Applications (CMLA – CNRS and ENS Paris-Saclay). He also leads a research group on Machine Learning and Massive Data Analysis (MLMDA) of about 20 people which is highly involved in interdisciplinary projects in the areas of network science, healthcare, digital marketing, and scientific computing. His main research interests are machine learning theory and algorithms, predictive modeling, sequential optimization and inference problems arising from real graph data. Nicolas Vayatis has been the advisor of 15 PhD students (defended) and 11 postdoctoral researchers between 2009 and 2017 and has coauthored more than 90 publications in peer-reviewed international journals and conferences. He also serves as an Action Editor for the Journal of Machine Learning Research since 2007. He is regularly invited to participate to scientific committees for the main conferences of the field of machine learning (NIPS, COLT, ALT,…). Over the years, Nicolas has been intensively teaching applied mathematics, statistics and probability, data mining and statistical learning for engineers, maths students, economists and psychologists, at various institutions (Université Pierre-et- Marie-Curie, ENS Cachan, Ecole Centrale Paris, ENSAE, INSEAD, Université Paris Nanterre, GeorgiaTech, Universitat Pompeu Fabra), and he currently is the main coordinator of the graduate master program MVA on Mathematics, Vision, and Learning which offers high level research training for more than 150 students per year. Nicolas Vayatis also provides expertise as a scientific advisor for the French Nuclear Agency (CEA).

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